# The `params` object is available in the document.
data_acc_sps_prov <- data |>
dplyr::filter(species_code == params$species_code,
subnational1_code %in% params$provinces) |> # User input
dplyr::mutate(yearmon = as.yearmon(as.Date(date))) |>
dplyr::group_by(yearmon, subnational1_code) |>
dplyr::summarize(n = dplyr::n()) |>
ungroup() |>
group_by(subnational1_code) |>
dplyr::mutate(cumsum = cumsum(n))
## `summarise()` has grouped output by 'yearmon'. You can override using the
## `.groups` argument.
## LINEPLOT
plotly::ggplotly(
data_acc_sps_prov |>
ggplot2::ggplot(aes(x = as.Date(yearmon),
y = cumsum,
color = subnational1_code)) +
ggplot2::geom_line(size = 0.5,
alpha = 0.5) +
ggplot2::geom_point(alpha = 0.5) +
ggplot2::scale_x_date(date_breaks = "1 month",
date_labels = "%b %Y") +
# ggplot2::scale_color_brewer(palette = "Set2") +
ggplot2::labs(title = paste('Period', min(data$date),
"to",
max(data$date)),
x = 'date',
y = 'observations')
)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## BOXPLOT
data |>
dplyr::filter(species_code == params$species_code,
subnational1_code %in% params$provinces) |> # User input
dplyr::mutate(yearmon = as.yearmon(as.Date(date))) |>
mutate(month = factor(format(yearmon, "%b"),
levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))) |>
pivot_longer(starts_with("fed_in"),
values_to = 'fed_in_val' ,
names_to= 'fed_in_names' ) |>
ggplot(aes(month, how_many)) +
geom_boxplot()+
geom_jitter(aes(color = subnational1_code))
